Applied Statistics for Life Sciences

Updated

April 22, 2025

Statistics plays a crucial role in the sciences: statistical techniques provide a means of weighing quantitative evidence derived from observation and experimentation while accounting for uncertainty. This class aims to provide a hands-on introduction to common statistical methods used almost universally across the sciences and a primer on statistical concepts. Examples from the life sciences emphasize applications with relevance to students’ majors, and students learn to perform simple analyses in R.

Read the [course syllabus] for more information.

Announcements

Test 1 scores posted; corrections due Friday 4/25/25 11:59pm PDT.

Reminder: HW4 (completion of lab 5) due Tuesday 4/22/25 11:59pm PDT.

Instructor: Trevor Ruiz (he/him) [email]

Learning assistant: Emi Degembe (she/they) [email]

Class meetings:

Office hours and learning assistant hours:

Preparing for class meetings:

  1. Complete any outstanding problems or other work from prior class meetings; these should be submitted by the start of class.
  2. Check the course website for posted reading and materials. Readings should be skimmed in advance of class meetings and read in depth after class meetings.

Week 1 (3/31/25)

Tuesday: study design and data semantics

  • [reading] Vu and Harrington 1.1 - 1.3
  • [lecture] course intro; study designs and data semantics
  • [lab] R basics [solutions]

Thursday: descriptive statistics

Week 2 (4/7/25)

Tuesday: point estimation

  • [reading] Vu and Harrington 4.1
  • [lecture] point estimation and sampling variability
  • [lab] point and interval estimation for a population mean [solutions]
  • [HW2] due next class [prompts] [submit] [solutions]

Thursday: interval estimation

  • [reading] Vu and Harrington 3.3.1, 3.3.2, and 3.3.3; and 4.2
  • [lecture] confidence interval coverage and critical values
  • [lab] computing critical values [solutions]
  • [HW3] due next class [prompts] [submit] [solutions]

Week 3 (4/14/25)

Tuesday: one-sample inference for a population mean

  • [reading] Vu and Harrington 4.3.1-4.3.4
  • [lecture] intro to hypothesis testing
  • [lab] one-sample t-tests in R [solutions]
  • [HW4] finish lab activity by next class [submit]

Thursday: test 1 review

Week 4 (4/21/25)

Tuesday: two-sample inference for a difference in population means

  • [reading] Vu and Harrington 5.3-5.4
  • [lecture] two-sample inference; statistical power
  • [lab] two-sample t tests in R
  • [HW5] due next class [prompts] [submit]

Thursday: analysis of variance (ANOVA)

  • [reading] Vu and Harrington 5.5.1 & 5.5.2

Week 5 (4/28/25)

Tuesday: post-hoc inference in ANOVA

  • [reading] Vu and Harrington 5.5.3 & 5.5.4

Thursday: test 2 review

Week 6 (5/5/25)

Tuesday: test 2

Thursday: nonparametric inference

  • [reading] van Belle et al. 8.4 and 8.5 up to 8.5.4

Week 7 (5/12/25)

Tuesday: inference for proportions

  • [reading] Vu and Harrington 8.1 & 8.2

Thursday: analysis of contingency tables I

  • [reading] Vu and Harrington 8.3 (excluding 8.3.5), 8.5

Week 8 (5/19/25)

Tuesday: analysis of contingency tables II

  • [reading] Vu and Harrington 8.3.5, 8.4

Thursday: test 3 review

Week 9 (5/26/25)

Memorial Day observed – no classes Monday and Tuesday follows a Monday schedule.

Tuesday: no class meeting

Thursday: simple linear regression

  • [reading] Vu and Harrington 6.1 & 6.2

Week 10 (6/2/24)

Tuesday: inference in regression

  • [reading] Vu and Harrington 6.4 & 6.5

Thursday: review for final

Exam info

Scheduled tests:

  • Test 1: take home due Friday 4/18/25
  • Test 2: in class Tuesday 5/6/25
  • Test 3: take home due Friday 5/23/25
  • Final: common final Saturday 6/7/25 (time and location TBD)

Resources: